MOCCA (version 1.1)

mocca: Multi-objective optimization for collecting cluster alternatives

Description

Performs a multi-objective optimization for collecting cluster alternatives. The algorithm draws R bootstrap samples from x. It calculates clusterings for all specified cluster numbers K using k-means, neuralgas, and single-linkage clustering. It then applies several cluster validation indices to the clusterings.

Usage

mocca(x, R = 50, K = 2:10, iter.max = 1000, nstart = 10)

Arguments

x

A numeric matrix of data, or an object that can be coerced to such a matrix (such as a numeric vector or a data frame with numeric columns).

R

The number of bootstrap samples.

K

The range of cluster numbers, i.e. a vector of integers listing the maximum numbers of clusters to be used by each of the algorithms.

iter.max

The maximum number of iterations allowed in k-means.

nstart

For k-means, how many random sets should be chosen?

Value

• A list with two entries:
• cluster
  A list containing one sublist for each clustering algorithm and the baseline cluster solution. Each of these lists hold an entry for each cluster size K, which again consists of R vectors of cluster assignments. These vectors assign each data point in x to a cluster.
• objectiveVals
  A matrix of objective function values. Each row corresponds to a certain cluster validation index applied to a certain clustering algorithm. The columns correspond to different cluster numbers. Consequently, an entry of the matrix specifies the median value of a certain cluster validation index for a certain clustering algorithm with a specific number of clusters over the R bootstrap samples.

Examples

data(toy5)
res <- mocca(toy5, R=10, K=2:5)
print(res$objectiveVals)
# plot kmeans result for MCA index against neuralgas result for MCA index
plot(res$objectiveVals[1,], res$objectiveVals[5,], pch=NA,
xlab=rownames(res$objectiveVals)[1], ylab=rownames(res$objectiveVals)[5])
text(res$objectiveVals[1,], res$objectiveVals[5,], labels=colnames(res$objectiveVals))